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Feedback control is a conceptual pattern from control theory that measures and adjusts system behavior via feedback to achieve setpoints or reject disturbances. It defines measurement, comparison and corrective-action loops and supports stability and robustness. It is applicable across software architecture, operations and product quality.
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Feedback control is a control principle in which the current state, a target value, and corrective action are linked in a feedback loop so systems remain stable or compensate for disturbances.
The concept comes from control theory and cybernetics, which studied how processes can stay stable despite changing influences. Historical precursors reach back to self-regulating mechanisms such as float valves and governors; in systems and software practice, this became the model of a closed loop of measuring, comparing, and intervening.
Think of a thermostat: a sensor measures the current value, a controller compares it with the target value, and an actuator changes the control input. The effect returns later as a new measurement. This lets the system correct deviations step by step. If the feedback is too strong, too slow, or too noisy, the system may oscillate instead of settling.
Output and observation form a circle in which the effect triggers new action.
A sensor or metric provides the system's current state.
The desired target state serves as the reference for regulation.
The control element decides how to correct based on deviation and context.
A change to resources, parameters, or behavior influences the system.
External influences push the system away from the target state.
Feedback control is useful when systems must regulate load shifts, latency, quality targets, or safety limits, for example in autoscaling, rate limiting, cache control, or SLO-based alerting. It works best with reliable measurements and adjustable interventions; delays, poor signals, or excessive gain can cause oscillation, overcorrection, and extra operational work.
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